IIARD International Institute of Academic Research and Development

International Journal of Engineering and Modern Technology (IJEMT )

E-ISSN 2504-8848
P-ISSN 2695-2149
VOL. 11 NO. 4 2025
DOI: 10.56201/ijemt.vol.11.no5.2025.pg1.10

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Metaheuristic-Based Predictive Modelling of Wind Velocity Distributions

Ekeoma, Chukwuma George, Nwaogu Chibuzo Jackie, Esenamunjor, Clement, Temidayo

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Abstract

Accurate modeling of wind velocity distribution is essential for optimizing wind energy generation and improving resource assessments. This study presents a predictive stochastic model utilizing the Weibull distribution, with its parameters optimized using the Particle Swarm Optimization (PSO) algorithm—a robust metaheuristic technique. Wind speed data collected over a one-year period in the Aba region were analyzed. The PSO-based estimation significantly outperformed traditional methods like Maximum Likelihood Estimation (MLE), yielding lower error margins and higher correlation with empirical data. The optimized model achieved a high coefficient of determination (R² = 0.972) and a reduced Root Mean Square Error (RMSE = 0.0087), confirming its effectiveness. The study demonstrates the potential of PSO-enhanced models in supporting reliable wind energy resource evaluation and system design.

keywords:

Wind Speed Modeling, Weibull Distribution, Particle Swarm Optimization, Metaheuristics, Renewable Energy, Stochastic Modeling

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